<title>Motion feature extraction scheme for content-based video retrieval</title>

There has been a revolution in multimedia with technological advancement. Hence, Video recording has increased in leaps and bounds. Video retrieval from a huge database is cumbersome by the existing text based search since a lot of human effort is involved and the retrieval efficiency is meager as well. In view of the present challenges, video retrieval based on video content prevails over the existing conventional methods. Content implies real video information such as video features. The performance of the Content Based Video Retrieval (CBVR) depends on Feature extraction and similar features matching. Since the selection of features in the existing algorithms is not effective, the retrieval processing time is more and the efficiency is less. Combined features of color and motion have been proposed for feature extraction and Spatio-Temporal Scale Invariant Feature Transform is used for Shot Boundary Detection. Since the characteristic of color feature is visual video content and that of motion feature is temporal content, these two features are significant in effective video retrieval. The performance of the CBVR system has been evaluated on the TRECVID dataset and the retrieved videos reveal the effectiveness of proposed algorithm.

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Content based Video Retrieval using Enhance Feature Extraction

International Journal of Computer Applications ◽

10.5120/21173-4052 ◽

2015 ◽

Vol 119 (19) ◽

pp. 4-8

Author(s):

Dipika HPatel

Keyword(s):

Feature Extraction ◽

Video Retrieval ◽

Content Based Video Retrieval

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A Video Retrieval System with Orthogonal Polynomials Model based Shot Detection, Key Frame Selection and Color Feature Extraction

Journal of Research on the Lepidoptera ◽

10.36872/lepi/v51i2/301112 ◽

2020 ◽

Vol 51 (2) ◽

pp. 453-479

Author(s):

BRAVEEN M.

Keyword(s):

Feature Extraction ◽

Orthogonal Polynomials ◽

Retrieval System ◽

Video Retrieval ◽

Color Feature ◽

Frame Selection ◽

Key Frame ◽

Model Based ◽

Shot Detection ◽

Key Frame Selection

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iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins

Letters in Organic Chemistry ◽

10.2174/1570178615666180816101653 ◽

2019 ◽

Vol 16 (4) ◽

pp. 294-302 ◽

Cited By ~ 6

Author(s):

Shahid Akbar ◽

Maqsood Hayat ◽

Muhammad Kabir ◽

Muhammad Iqbal

Keyword(s):

Feature Extraction ◽

Amino Acid ◽

Antifreeze Proteins ◽

Protein Sequences ◽

Sampling Technique ◽

Lower Class ◽

Success Rates ◽

Throughput Model ◽

Extraction Scheme ◽

Living Organisms

Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.

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